Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Advertisement

Scientific Reports
  • View all journals
  • Search
  • My Account Login
  • Content Explore content
  • About the journal
  • Publish with us
  • Sign up for alerts
  • RSS feed
  1. nature
  2. scientific reports
  3. articles
  4. article
Energy-efficient wireless sensor network for urban groundwater level monitoring using machine learning and sink mobility
Download PDF
Download PDF
  • Article
  • Open access
  • Published: 17 February 2026

Energy-efficient wireless sensor network for urban groundwater level monitoring using machine learning and sink mobility

  • Rachit Manchanda1,
  • Ailaboina Vijaya Lakshmi2,
  • Gaganjot Kaur3,
  • Gadug Sudhamsu4,
  • Satish Kumar Samal5,
  • G. D. Anbarasi Jebaselvi6,7,
  • Ranjan Kumar8,
  • Abhijit Bhowmik9,10 &
  • …
  • N. Ashok11 

Scientific Reports , Article number:  (2026) Cite this article

  • 331 Accesses

  • Metrics details

We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

Subjects

  • Engineering
  • Environmental sciences
  • Mathematics and computing

Abstract

Urban groundwater level monitoring is vital for enabling data-driven decision-making and sustainable urban water resource management. Wireless Sensor Networks (WSNs) offer an effective solution for real-time observation of spatially distributed underground water sources. However, conventional WSN protocols often face significant limitations, such as unbalanced data routing, excessive energy consumption, and the energy hole problem near the sink. To overcome these challenges, this paper proposes an energy-efficient WSN protocol named Sleep Scheduled Data Aggregation with Sink Mobility (SSDA-SM), specifically designed for Urban Groundwater Monitoring (UGM) in heterogeneous sensor networks. The protocol incorporates a machine learning (ML)-based probabilistic clustering mechanism to optimize Cluster Head (CH) selection, considering residual energy, node density, and average network energy. To further conserve energy, a proximity-aware sleep scheduling strategy selectively deactivates redundant nodes, while dynamic sink mobility uniformly balances communication load and mitigates the energy hole problem. Moreover, to reduce transmission overhead, Compressive Sensing (CS) is applied at the CH level for data aggregation, and the original data is accurately reconstructed at the sink using an appropriate decoding algorithm. The SSDA-SM protocol is implemented and simulated in MATLAB. Performance evaluation shows that SSDA-SM significantly outperforms existing protocols such as OCNTMS, MEDF, SEI\(^{2}\), and MACOA across various metrics including network lifetime, energy consumption per round, data throughput, packet delivery ratio, end-to-end delay, cluster stability, compression ratio, and reconstruction accuracy. These results demonstrate that SSDA-SM is a robust, scalable, and energy-efficient solution for long-term urban groundwater level monitoring using heterogeneous WSNs.

Data availability

The data that support the findings of this study are available from the corresponding author, [NA], upon reasonable request.

Abbreviations

ANN:

Artificial neural network

DEC-KM :

Distance–energy–centroid k-means

IK-MACHES :

Improved K-means–mobility aware cluster head election scheme

IS-k-means :

Improved soft-k-means clustering algorithm

LEACH-RLC :

Low-energy adaptive clustering hierarchy–reinforcement learning based clustering

MACOA :

Multi-objective ant colony optimization algorithm

MEDF :

Mobile Energy–aware data forwarding

ML-CH Hybrid :

Machine learning–based cluster head hybrid

MOCRAW :

Mobile Sink–based clustering and routing algorithm using whale optimization

OCNTMS :

Optimized clustering using node threshold and mobile sink

SEI\(^{2}\) :

Secure and energy-efficient infrastructure

SVM + RF Ensemble :

Support vector machine + random forest ensemble

PSO :

Particle swarm optimization

GWO :

Grey Wolf optimization

DA :

Dragonfly algorithm

A-HDAC :

Adaptive hierarchical data aggregation and compressive sensing

NSPL-HCS :

Non-sparse projection learning–hybrid compressive sensing

UWSNs :

Underwater wireless sensor networks

BLDCSSA-CDG :

Balanced load distributed clustering with sleep scheduling algorithm – centralized data gathering

NDP :

Node Disjoint paths

TSCH :

Time slotted channel hopping

TASA :

Time slotted channel hopping with adaptive scheduling algorithm

SEED-style :

Sleep energy-efficient duty-cycle style

References

  1. Venkatesh, J., Partheeban, P., Baskaran, A., Krishnan, D. & Sridhar, M. Wireless sensor network technology and geospatial technology for groundwater quality monitoring. J. Ind. Inf. Integr. 38, 100569 (2024).

    Google Scholar 

  2. Aburukba, R. et al. Wireless Sensor Networks for Urban Development: A Study of Applications, Challenges, and Performance Metrics. Smart Cities 8(3), 89 (2025).

    Google Scholar 

  3. Bhasker, B. & Murali, S. An Energy-Efficient Cluster-based data aggregation for agriculture irrigation management system using wireless sensor networks. Sustain. Energy Technol. Assess. 65, 103771 (2024).

    Google Scholar 

  4. Alhazemi, F. Sequential clustering phases for environmental noise level monitoring on a mobile crowd sourcing/sensing platform. Sensors 25(5), 1601 (2025).

    Google Scholar 

  5. Tong, S. & Peng, J. Providing an optimal method for clustering in wireless sensor networks based on the Q-LEACH protocol. Multisc. Multidiscipl. Model. Exp. Des. 8(6), 294 (2025).

    Google Scholar 

  6. Prabu, R. T. et al. IoT-enabled groundwater monitoring with k-NN-SVM algorithm for sustainable water management. Acta Geophys. 72(4), 2715–2728 (2024).

    Google Scholar 

  7. Juwaied, A., Jackowska-Strumillo, L. & Sierszeń, A. Enhancing clustering efficiency in heterogeneous wireless sensor network protocols using the k-nearest neighbours algorithm. Sensors 25(4), 1029 (2025).

    Google Scholar 

  8. Nathiya, N., Rajan, C. & Geetha, K. A hybrid optimization and machine learning based energy-efficient clustering algorithm with self-diagnosis data fault detection and prediction for WSN-IoT application. Peer-to-Peer Netw. Appl. 18(2), 13 (2025).

    Google Scholar 

  9. Sharma, A. & Kansal, A. Enhanced CH selection and energy efficient routing algorithm for WSN. Microsyst. Technol. 31(3), 735–747 (2025).

    Google Scholar 

  10. Gupta, D. et al. Optimizing cluster head selection for e-commerce-enabled wireless sensor networks. IEEE Trans. Consum. Electron. 70(1), 1640–1647 (2024).

    Google Scholar 

  11. Lewandowski, M. & Płaczek, B. A cluster head selection algorithm for extending last node lifetime in wireless sensor networks. Sensors 25(11), 3466 (2025).

    Google Scholar 

  12. Jalili, A. et al. A novel model for efficient cluster head selection in mobile WSNs using residual energy and neural networks. Meas. Sensors 33, 101144 (2024).

    Google Scholar 

  13. Balamurali, S., Kathirvelu, M., Palanisamy, S. & Jaghdam, I. H. Redefining IoT networks for improving energy and memory efficiency through compressive sensing paradigm. Sci. Rep. 15(1), 27180 (2025).

    Google Scholar 

  14. Tabatabaei, S. A fault-tolerant clustering approach for target tracking in wireless sensor networks. Wireless Pers. Commun. 137(4), 2303–2322 (2024).

    Google Scholar 

  15. Zhang, S., Liu, X. & Trik, M. Energy efficient multi hop clustering using Artificial Bee Colony metaheuristic in WSN. Sci. Rep. 15(1), 26803 (2025).

    Google Scholar 

  16. Joon, R., Tomar, P., Kumar, G., Balusamy, B. & Nayyar, A. Unequal clustering energy hole avoidance (UCEHA) algorithm in cognitive radio wireless sensor networks (CRWSNs). Wireless Netw. 31(1), 735–757 (2025).

    Google Scholar 

  17. Singh, P. & Vir, R. Enhanced energy-aware routing protocol with mobile sink optimization for wireless sensor networks. Comput. Netw. 261, 111100 (2025).

    Google Scholar 

  18. Yuan, H. & Gao, C. Minimizing redundancy in wireless sensor networks using sparse vectors. Sensors 25(5), 1557 (2025).

    Google Scholar 

  19. Roberts, M. K., Jeevanandham, S., Lloret, J. & Dahan, F. An innovative dual-phased synergistic energy management approach for WSNs using enhanced sleep/awake scheduling and adaptive routing process. Simul. Model. Pract. Theory 142, 103120 (2025).

    Google Scholar 

  20. Bengheni, A. Relay node selection scheme and deep sleep period for power management in energy-harvesting wireless sensor networks. Int. J. Commun. Syst. 37(8), e5742 (2024).

    Google Scholar 

  21. Hema, L. K. & Raj, R. S. Enhancing energy-efficient sleep scheduling for Narrowband Internet of Things devices in coordinated 5G networks within smart environments. Int. J. Commun. Syst. 37(9), e5773 (2024).

    Google Scholar 

  22. Singh, Y. & Walingo, T. Smart water quality monitoring with IoT wireless sensor networks. Sensors 24(9), 2871 (2024).

    Google Scholar 

  23. Nguyen, H.-H.-D., Pradhan, A. M. S., Song, C.-H., Lee, J.-S. & Kim, Y.-T. A hybrid approach combining physics-based model with extreme value analysis for temporal probability of rainfall-triggered landslide. Landslides 22(1), 149–168 (2025).

    Google Scholar 

  24. Sanhaji, F., Affane, M. A. R., Satori, H. & Satori, K. Intelligent cluster head selection for energy-efficient wireless sensor networks: An MLP-based approach. Wireless Pers. Commun. 139(4), 1981–2001 (2024).

    Google Scholar 

  25. Jurado-Lasso, F. F., Jurado, J. F. & Fafoutis, X. LEACH-RLC: Enhancing IoT data transmission with optimized clustering and reinforcement learning. IEEE Internet Things J. 12, 23462–23478 (2025).

    Google Scholar 

  26. Wang, M., Chen, H., Wang, Y. & Chen, W. Improved soft-k-means clustering charging based on node collaborative scheduling in wireless sensor networks. Wireless Pers. Commun. 137(4), 2487–2513 (2024).

    Google Scholar 

  27. Prompook, T. et al. Impact of distance measures in adaptive K-means clustering on load profiles and spatial patterns of distributed substations in Thailand. Sci. Rep. 15(1), 21123 (2025).

    Google Scholar 

  28. Hada, R. P. S. & Srivastava, A. Dynamic cluster head selection in WSN. ACM Trans. Embedded Comput. Syst. 23(4), 1–27 (2024).

    Google Scholar 

  29. Senturk, A. Artificial neural networks-based LEACH algorithm for fast and efficient cluster head selection in wireless sensor networks. Int. J. Commun Syst 38(3), e6127 (2025).

    Google Scholar 

  30. Ambareesh, S. et al. A secure and energy-efficient routing using coupled ensemble selection approach and optimal type-2 fuzzy logic in WSN. Sci. Rep. 15(1), 38 (2025).

    Google Scholar 

  31. Asif, S., Wenhui, Y. & ul Ain, Q., Yueyang, Y., Jinhai, S. Improving the accuracy of diagnosing and predicting coronary heart disease using ensemble method and feature selection techniques. Clust. Comput. 27(2), 1927–1946 (2024).

    Google Scholar 

  32. Chaurasia, S. & Kumar, K. MBASE: Meta-heuristic based optimized location allocation algorithm for baSE station in IoT assist wireless sensor networks. Multimedia Tools Appl. 83(18), 53383–53415 (2024).

    Google Scholar 

  33. Li, H., Dai, Y., Chen, Q., Liao, D. & Jin, H. Energy efficient mobile sink driven data collection in wireless sensor network with nonuniform data. Sci. Rep. 14(1), 28190 (2024).

    Google Scholar 

  34. Hassan, E. S. et al. Energy-efficient data fusion in WSNs using mobility-aware compression and adaptive clustering. Technologies 12(12), 248 (2024).

    Google Scholar 

  35. Gharaei, N. & Alabdali, A. M. Secure and energy-efficient inter-and intra-cluster optimization scheme for smart cities using UAV-assisted wireless sensor networks. Sci. Rep. 15(1), 4190 (2025).

    Google Scholar 

  36. Amshavalli, R. S., Devi, D., Srinivasan, S., ShaliniRajan, R. & Jebamani, S. A. Boosted sooty tern and Pranhav foraging meta-heuristic optimized cluster head selection-based routing algorithm for extending network lifetime in WSNs. Peer-to-Peer Netw. Appl. 18(2), 66 (2025).

    Google Scholar 

  37. Liu, Z. et al. WSNs data acquisition by combining expected network coverage and clustered compressed sensing. PLoS ONE 20(6), e0326078 (2025).

    Google Scholar 

  38. Palanisamy, S. et al. A novel Hadamard matrix based hybrid compressive sensing technique for enhancing energy efficiency and network longevity. Sci. Rep. 15(1), 1–20 (2025).

    Google Scholar 

  39. El-Shenhabi, A. N., Abdelhay, E. H., Mohamed, M. A. & Moawad, I. F. A reinforcement learning-based dynamic clustering of sleep scheduling algorithm (RLDCSSA-CDG) for compressive data gathering in wireless sensor networks. Technologies 13(1), 25 (2025).

    Google Scholar 

  40. Tong, Y. et al. Coverage optimization and node minimization in WSNs: an enhanced hybrid PSO approach with spatial position encoding. Sci. Rep. 15(1), 25332 (2025).

    Google Scholar 

  41. Gao, X., Yao, X., Chen, B. & Zhang, H. SBCS-Net: Sparse Bayesian and deep learning framework for compressed sensing in sensor networks. Sensors 25(15), 4559 (2025).

    Google Scholar 

  42. He, Z., He, R., Ai, B., Zhang, H. & Zhong, Z. Joint angle-delay sparse structured compressed sensing for ISAC channel estimation. IEEE Trans. Veh. Technol. https://doi.org/10.1109/TVT.2025.3557767 (2025).

    Google Scholar 

  43. Ketshabetswe, L. K., Zungeru, A. M., Lebekwe, C. K. & Mtengi, B. A compression-based routing strategy for energy saving in wireless sensor networks. Results Eng. 23, 102616 (2024).

    Google Scholar 

  44. Handuo, H. et al. Investigation on uncertainty quantification of transonic airfoil using compressive sensing greedy reconstruction algorithms. Aerosp. Sci. Technol. 147, 109000 (2024).

    Google Scholar 

  45. Kumar, J. D. S.; Subramanyam, M. V.; Kumar, A. P. S. (2024). Hybrid Sand Cat Swarm Optimization Algorithm-based reliable coverage optimization strategy for heterogeneous wireless sensor networks. International Journal of Information Technology, 1–19.

  46. Tharmalingam, R., Nachimuthu, N. & Prakash, G. An efficient energy supply policy and optimized self-adaptive data aggregation with deep learning in heterogeneous wireless sensor network. Peer-to-Peer Netw. Appl. 17(6), 3991–4012 (2024).

    Google Scholar 

  47. Khan, M. N., Lee, S. & Shah, M. Adaptive scheduling in cognitive IoT sensors for optimizing network performance using reinforcement learning. Appl. Sci. 15(10), 5573 (2025).

    Google Scholar 

  48. Zhang, Y., Xiao, X. & Guo, J. TransMCS: A hybrid CNN-transformer autoencoder for end-to-end multi-modal medical signals compressive sensing. Theor. Comput. Sci. 1051, 115409 (2025).

    Google Scholar 

  49. Eren, H., Karaduman, Ö. & Gençoğlu, M. T. Security challenges and performance trade-offs in on-chain and off-chain blockchain storage: A comprehensive review. Appl. Sci. 15(6), 3225 (2025).

    Google Scholar 

  50. Singh, N. & Adhikari, M. Integrated probabilistic clustering and Deep Reinforcement Learning for bias mitigation and device heterogeneity of Federated Learning in edge networks. J. Netw. Comput. Appl. 242, 104259 (2025).

    Google Scholar 

  51. Li, N. et al. Performance and energy consumption analysis for UWSNs with priority scheduling based on access probability and wakeup threshold. Sensors 25(2), 570 (2025).

    Google Scholar 

  52. Zaier, A., Lahmar, I., Yahia, M. & Lloret, J. Interval type 2 fuzzy unequal clustering and sleep scheduling for IoT-based WSNs. Ad Hoc Netw. 175, 103867 (2025).

    Google Scholar 

  53. Rajput, N. et al. Deep Q-learning driven protocol for enhanced border surveillance with extended wireless sensor network lifespan. CMES-Comput. Model. Eng. Sci. 143(3), 3839–3859 (2025).

    Google Scholar 

  54. Hameed, M. K. & Idrees, A. K. Energy-aware scheduling protocol-based hybrid metaheuristic technique to optimize the lifespan in WSNs. J. Supercomput. 80(9), 12706–12726 (2024).

    Google Scholar 

  55. Lee, W., Youn, J.-H. & Song, T.-S. Asymmetric wake-up scheduling based on block designs for Internet of Things. Ad Hoc Netw. 162, 103530 (2024).

    Google Scholar 

  56. Vatankhah, A. & Liscano, R. Comparative analysis of time-slotted channel hopping schedule optimization using priority-based customized differential evolution algorithm in heterogeneous IoT networks. Sensors 24(4), 1085 (2024).

    Google Scholar 

  57. Shaheen, Z., Sattar, K. & Ahmed, M. Pairing algorithm for varying data in cluster based heterogeneous wireless sensor networks. PeerJ Comput. Sci. 10, e2243 (2024).

    Google Scholar 

  58. Li, J., Wang, H. & Xiao, W. A reinforcement learning based mobile charging sequence scheduling algorithm for optimal sensing coverage in wireless rechargeable sensor networks. J. Ambient. Intell. Humaniz. Comput. 15(6), 2869–2881 (2024).

    Google Scholar 

  59. Mutar, M. S., Hamza, Z. A., Hammood, D. A. & Hashem, S. A. A survey of sleep scheduling techniques in wireless sensor networks for maximizing energy efficiency. AIP Conf. Proc. 3232(1), 020058 (2024).

    Google Scholar 

  60. Kumar, V., Singla, S., Arora, S., Keshari, S. K. & Kumar, S. Energy efficient optimized sleep scheduling routing protocol for enhancement of MANET lifetime. Wireless Pers. Commun. 136(3), 1849–1877 (2024).

    Google Scholar 

  61. Rawat, P., Rawat, G. S., Rawat, H. & Chauhan, S. Energy-efficient cluster-based routing protocol for heterogeneous wireless sensor network. Ann. Telecommun. 80(1), 109–122 (2025).

    Google Scholar 

  62. Singh, H. et al. Artificial neural network modeling and experimental analysis of erosion resistance in tungsten carbide-coated CA6NM stainless steel. Int. J. Adv. Manuf. Technol. 50, 1–21 (2025).

    Google Scholar 

  63. Kumar, V. et al. Enhanced clustering approach for efficient relay vehicle selection in vehicular ad hoc networks. Sci. Rep. 15(1), 38775 (2025).

    Google Scholar 

  64. Abdullah, M. I. et al. Numerical modeling and performance optimization of all inorganic Pb-free novel NaSnCl\(_3\)-based perovskite solar cells via SCAPS-1D framework. Sci. Rep. 15(1), 41709 (2025).

    Google Scholar 

  65. Kanchi, S. Clustering algorithm for wireless sensor networks with balanced cluster size. Procedia Comput. Sci. 238, 119–126 (2024).

    Google Scholar 

  66. Wang, W., Chen, J. & Zhang, Y. Adaptive compressive sensing based on sparsity order estimation for wireless image sensor networks. IEEE Sens. J. 24(13), 21132–21142 (2024).

    Google Scholar 

  67. Prince, B., Kumar, P. & Singh, S. K. Multi-level clustering and Prediction based energy efficient routing protocol to eliminate Hotspot problem in Wireless Sensor Networks. Sci. Rep. 15(1), 1122 (2025).

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science Engineering, University Institute of Engineering, Chandigarh University, Mohali, Punjab, 140413, India

    Rachit Manchanda

  2. Department of ECE, Vardhaman College of Engineering, Hyderabad, Telangana, India

    Ailaboina Vijaya Lakshmi

  3. Department of Electronics and Communication Engineering, Chandigarh University, Mohali, Punjab, India

    Gaganjot Kaur

  4. Department of Computer Science and Engineering, School of Engineering and Technology, JAIN (Deemed to be University), Bangalore, Karnataka, 560002, India

    Gadug Sudhamsu

  5. Department of Electronics & Communication Engineering, Siksha ’O’ Anusandhan (Deemed to be University), Bhubaneswar, Odisha, 751030, India

    Satish Kumar Samal

  6. Department of Electronics and Communication Engineering, Sathyabama Institute of Science and Technology, Chennai, Tamil Nadu, India

    G. D. Anbarasi Jebaselvi

  7. Sharda School of Engineering and Sciences, Sharda University, Greater Noida, Uttar Pradesh, India

    G. D. Anbarasi Jebaselvi

  8. Department of Electronics and Communication Engineering, Noida Institute of Engineering & Technology, Knowledge Park-II, Greater Noida, India

    Ranjan Kumar

  9. Department of Additive Manufacturing, Mechanical Engineering, SIMATS, Saveetha Institute of Medical and Technical Sciences, Thandalam, Chennai, Tamil Nadu, 602105, India

    Abhijit Bhowmik

  10. Division of Research and Development, Lovely Professional University, Phagwara, Punjab, 144001, India

    Abhijit Bhowmik

  11. Faculty of Mechanical Engineering, Jimma Institute of Technology, Jimma, Ethiopia

    N. Ashok

Authors
  1. Rachit Manchanda
    View author publications

    Search author on:PubMed Google Scholar

  2. Ailaboina Vijaya Lakshmi
    View author publications

    Search author on:PubMed Google Scholar

  3. Gaganjot Kaur
    View author publications

    Search author on:PubMed Google Scholar

  4. Gadug Sudhamsu
    View author publications

    Search author on:PubMed Google Scholar

  5. Satish Kumar Samal
    View author publications

    Search author on:PubMed Google Scholar

  6. G. D. Anbarasi Jebaselvi
    View author publications

    Search author on:PubMed Google Scholar

  7. Ranjan Kumar
    View author publications

    Search author on:PubMed Google Scholar

  8. Abhijit Bhowmik
    View author publications

    Search author on:PubMed Google Scholar

  9. N. Ashok
    View author publications

    Search author on:PubMed Google Scholar

Contributions

Conceptualization, R.M., A.V.L. and N.A. ; methodology, A.J.G.D. , R.K. and A.B. ; software, A.J.G.D. , R.K. and A.B. ; validation, G.K., G.S. and S.K.S. ; formal analysis, A.J.G.D. , R.K. and A.B. ; data curation, R.M. , A.V.L. and N.A. ; writing-original draft preparation, R.M. , A.V.L. and N.A. ; writing-review and editing, G.K., G.S. and S.K.S. ; visualization, G.K., G.S. and S.K.S. ; supervision, A.J.G.D. , R.K. and A.B. ; project administration, A.J.G.D. , R.K. and A.B. All authors have read and agreed to the published version of the manuscript.

Corresponding author

Correspondence to N. Ashok.

Ethics declarations

Competing interests

The authors declare no competing interests.

Disclosure

This study was performed as part of the employment of the authors.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Manchanda, R., Lakshmi, A.V., Kaur, G. et al. Energy-efficient wireless sensor network for urban groundwater level monitoring using machine learning and sink mobility. Sci Rep (2026). https://doi.org/10.1038/s41598-026-39435-1

Download citation

  • Received: 10 October 2025

  • Accepted: 05 February 2026

  • Published: 17 February 2026

  • DOI: https://doi.org/10.1038/s41598-026-39435-1

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Keywords

  • Wireless sensor networks
  • Urban groundwater monitoring
  • Machine learning
  • Compressive sensing
  • Cluster head
  • Data aggregation
  • Packet delivery ratio
Download PDF

Advertisement

Explore content

  • Research articles
  • News & Comment
  • Collections
  • Subjects
  • Follow us on Facebook
  • Follow us on X
  • Sign up for alerts
  • RSS feed

About the journal

  • About Scientific Reports
  • Contact
  • Journal policies
  • Guide to referees
  • Calls for Papers
  • Editor's Choice
  • Journal highlights
  • Open Access Fees and Funding

Publish with us

  • For authors
  • Language editing services
  • Open access funding
  • Submit manuscript

Search

Advanced search

Quick links

  • Explore articles by subject
  • Find a job
  • Guide to authors
  • Editorial policies

Scientific Reports (Sci Rep)

ISSN 2045-2322 (online)

nature.com sitemap

About Nature Portfolio

  • About us
  • Press releases
  • Press office
  • Contact us

Discover content

  • Journals A-Z
  • Articles by subject
  • protocols.io
  • Nature Index

Publishing policies

  • Nature portfolio policies
  • Open access

Author & Researcher services

  • Reprints & permissions
  • Research data
  • Language editing
  • Scientific editing
  • Nature Masterclasses
  • Research Solutions

Libraries & institutions

  • Librarian service & tools
  • Librarian portal
  • Open research
  • Recommend to library

Advertising & partnerships

  • Advertising
  • Partnerships & Services
  • Media kits
  • Branded content

Professional development

  • Nature Awards
  • Nature Careers
  • Nature Conferences

Regional websites

  • Nature Africa
  • Nature China
  • Nature India
  • Nature Japan
  • Nature Middle East
  • Privacy Policy
  • Use of cookies
  • Legal notice
  • Accessibility statement
  • Terms & Conditions
  • Your US state privacy rights
Springer Nature

© 2026 Springer Nature Limited

Nature Briefing AI and Robotics

Sign up for the Nature Briefing: AI and Robotics newsletter — what matters in AI and robotics research, free to your inbox weekly.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing: AI and Robotics